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+---
+layout: model
+title: Pipeline to Resolve Medication Codes
+author: John Snow Labs
+name: medication_resolver_pipeline
+date: 2023-04-10
+tags: [resolver, snomed, umls, rxnorm, ndc, ade, en, licensed, pipeline]
+task: Entity Resolution
+language: en
+edition: Healthcare NLP 4.3.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.
+
+Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.
+
+This pipeline can be used as Lightpipeline (with `annotate/fullAnnotate`). You can use `medication_resolver_transform_pipeline` for Spark transform.
+
+{:.btn-box}
+
+
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_4.3.2_3.0_1681151954032.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_4.3.2_3.0_1681151954032.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+med_resolver_pipeline = PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""
+
+result = med_resolver_pipeline.fullAnnotate(text)
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val med_resolver_pipeline = new PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+val result = med_resolver_pipeline.fullAnnotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.""")
+```
+
+
+## Results
+
+```bash
+| | chunks | entities | ADE | RxNorm | Action | Treatment | UMLS | SNOMED_CT | NDC_Product | NDC_Package |
+|---:|:-----------------------------|:-----------|:----------------------------|---------:|:---------------------------|:-------------------------------------------|:---------|:------------|:--------------|:--------------|
+| 0 | Amlodopine Vallarta 10-320mg | DRUG | Gynaecomastia | 722131 | NONE | NONE | C1949334 | 425838008 | 00093-7693 | 00093-7693-56 |
+| 1 | Eviplera | DRUG | Anxiety | 217010 | Inhibitory Bone Resorption | Osteoporosis | C0720318 | NONE | NONE | NONE |
+| 2 | Lescol 40 MG | DRUG | NONE | 103919 | Hypocholesterolemic | Heterozygous Familial Hypercholesterolemia | C0353573 | NONE | 00078-0234 | 00078-0234-05 |
+| 3 | Everolimus 1.5 mg tablet | DRUG | Acute myocardial infarction | 2056895 | NONE | NONE | C4723581 | NONE | 00054-0604 | 00054-0604-21 |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|medication_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 4.3.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.2 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- TextMatcherModel
+- ChunkMergeModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
+- ResolverMerger
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- Finisher
\ No newline at end of file
diff --git a/docs/_posts/SKocer/2023-04-11-medication_resolver_transform_pipeline_en.md b/docs/_posts/SKocer/2023-04-11-medication_resolver_transform_pipeline_en.md
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+---
+layout: model
+title: Pipeline to Resolve Medication Codes(Transform)
+author: John Snow Labs
+name: medication_resolver_transform_pipeline
+date: 2023-04-11
+tags: [resolver, rxnorm, ndc, snomed, umls, ade, pipeline, en, licensed]
+task: Entity Resolution
+language: en
+edition: Healthcare NLP 4.3.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.
+
+Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.
+
+This pipeline can be used with Spark transform. You can use `medication_resolver_pipeline` as Lightpipeline (with `annotate/fullAnnotate`).
+
+{:.btn-box}
+
+
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/medication_resolver_transform_pipeline_en_4.3.2_3.0_1681190723377.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_transform_pipeline_en_4.3.2_3.0_1681190723377.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+medication_resolver_pipeline = PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""
+
+data = spark.createDataFrame([[text]]).toDF("text")
+
+result = medication_resolver_pipeline.transform(data)
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val medication_resolver_pipeline = new PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+val data = Seq("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.""").toDS.toDF("text")
+
+val result = medication_resolver_pipeline.fit(data).transform(data)
+```
+